This is an open assessment looking at potential health effects of a national fish promotion program in Finland. The details of the assessment are described at Opasnet. This file contains the R code to run the assessment model.
This code downloads all the ovariables created by the previous code, fishhealth_initiate.Rmd, runs the model and plots various graphs and tables about the results.
objects.latest("Op_fi5932",code_name="initiate") # [[PFAS-yhdisteiden tautitaakka]]
## Loading objects:
## ages
## amount
## amount_raw
## amount_statistics
## amountOrig
## BF
## binoptest
## BoD
## BoDattr
## BoDattrOrig
## BW
## case_burden
## Colamount
## ColBoD
## ColBoDattr
## Colcase_burden
## Colconc_pfas
## Coldose
## ColERF
## Colexpo_dir
## Colexpo_indir
## Colexposure
## ColPAF
## Colthreshold
## conc
## conc_eukalat
## conc_mehg
## conc_pcddf
## conc_pcddf_raw
## conc_pfas
## conc_pfas_raw
## conc_vit
## CTable
## dat
## dec
## DecBoD
## Decconc
## Decconc_vit
## Decincidence
## df
## dose
## dummy
## ERF
## ERF_diox
## ERF_env
## ERF_mehg
## ERF_micr
## ERF_omega3
## ERF_pfas
## ERF_vit
## ERFchoice
## expand_index
## expo_background
## expo_dir
## expo_indir
## exposure
## f_ing
## f_mtoc
## fish_proportion
## frexposed
## Hg
## incidence
## Inpamount
## InpBoD
## limits
## mc2d
## mc2dparam
## opts
## ovashapetest
## P_illness
## PAF
## population
## prepare
## RR
## RRorig
## showind
## showLoctable
## subgrouping
## sumExposcen
## t0.5
## TEF
## TEFraw
## TEFversion
## tmp
## total_amount
## trim
## wiki_username
if(!params$porvoo) {
Decconc@dectable <- Decconc@dectable[Decconc@dectable$Option=="BAU",]
}
conc_eukalat <- EvalOutput(conc_eukalat)
BoDattr <- EvalOutput(BoDattr, verbose=TRUE)
## Evaluating BoDattr ...
## - Evaluating BoD ...
## - - Evaluating incidence ...
##
## done(0 secs)!
## - - Checking incidence marginals ... Response, Age, incidenceSource recognized as marginal(s).
## - - Processing incidence decisions ... done!
## - - Evaluating case_burden ...
##
## done(0.01 secs)!
## - - Checking case_burden marginals ... Response, Gender, Iter, case_burdenSource recognized as marginal(s).
## - - Processing case_burden marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm =
## TRUE): While oapplying case_burden, found NAs in indices: Gender. They were
## automatically filled using fillna, which may result in a multiplied population.
## Please check your ovariable before using oapply.
## done!
## - - Evaluating population ...
##
## done(0.01 secs)!
## - - Checking population marginals ... Gender, Age, populationSource recognized as marginal(s).
##
## - done(0.65 secs)!
## - Checking BoD marginals ... Response, Age, incidenceSource, Adjust, Gender, populationSource, Iter, BoDSource recognized as marginal(s).
## - Processing BoD inputs ... done!
## - Processing BoD marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying BoD, found NAs in indices: Adjust, InpBoDSource. They were
## automatically filled using fillna, which may result in a multiplied population.
## Please check your ovariable before using oapply.
## done!
## - Processing BoD decisions ... done!
## - Evaluating PAF ...
## - - Evaluating dose ...
## - - - Evaluating exposure ...
## - - - - Evaluating expo_dir ...
## - - - - - Evaluating amount ...
##
## ----- done(0.89 secs)!
## - - - - - Checking amount marginals ... Food, Iter, Group, Fish, Gender, fish_proportionSource, amountSource recognized as marginal(s).
## - - - - - Processing amount inputs ... done!
## - - - - - Processing amount marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm =
## TRUE): While oapplying amount, found NAs in indices: Iter, Group, Gender,
## fish_proportionSource, Fishing. They were automatically filled using fillna,
## which may result in a multiplied population. Please check your ovariable before
## using oapply.
## done!
## - - - - - Evaluating conc ...
## - - - - - - Evaluating conc_vit ...
##
## done(0.02 secs)!
## - - - - - - Checking conc_vit marginals ... Kala, Fish, Nutrient, Iter, conc_vitSource recognized as marginal(s).
## - - - - - - Processing conc_vit decisions ... done!
## - - - - - - Evaluating conc_pfas ...
## - - - - - - - Evaluating conc_pfas_raw ...
##
## done(0.09 secs)!
## - - - - - - - Checking conc_pfas_raw marginals ... Fish, Compound, Iter, conc_pfas_rawSource recognized as marginal(s).
##
## ------ done(1.09 secs)!
## - - - - - - Checking conc_pfas marginals ... Fish, Compound, Iter, conc_pfas_rawSource, Area, conc_pfasSource recognized as marginal(s).
## - - - - - - Processing conc_pfas marginal collapses ... done!
## - - - - - - Evaluating conc_pcddf ...
## - - - - - - - Evaluating conc_pcddf_raw ...
##
## done(0 secs)!
## - - - - - - - Checking conc_pcddf_raw marginals ... ﮮTHL_code, Matrix, POP, Fish_species, Catch_site, Catch_location, Catch_date, Catch_season, Catch_square, N_individuals, Sample_type, Length_mean_mm, Weight_mean_g, Sex, Age, Fat_percentage, Dry_matter_percentage, conc_pcddf_rawSource recognized as marginal(s).
## - - - - - - - Evaluating TEF ...
## - - - - - - - - Evaluating TEFraw ...
##
## done(0 secs)!
## - - - - - - - - Checking TEFraw marginals ... Group, Compound, TEFversion, TEFrawSource recognized as marginal(s).
##
## ------- done(0.06 secs)!
## - - - - - - - Checking TEF marginals ... Compound, Group, TEFversion, TEFrawSource, TEFSource recognized as marginal(s).
##
## ------ done(0.2 secs)!
## - - - - - - Checking conc_pcddf marginals ... Fish, conc_pcddfSource recognized as marginal(s).
## - - - - - - Evaluating conc_mehg ...
## Warning in rnorm(openv$N, Hg$MeanLog[i], Hg$SDLog[i]): NAs produced
## Warning in rnorm(openv$N, Hg$MeanLog[i], Hg$SDLog[i]): NAs produced
## ------ done(0.47 secs)!
## - - - - - - Checking conc_mehg marginals ... Area, Kala, Iter, Fish, conc_mehgSource recognized as marginal(s).
##
## ----- done(3.09 secs)!
## - - - - - Checking conc marginals ... Fish, Compound, Iter, concSource recognized as marginal(s).
## - - - - - Processing conc decisions ... done!
## - - - - - Evaluating expo_background ...
##
## done(0 secs)!
## - - - - - Checking expo_background marginals ... Exposure_agent, Gender, Iter, expo_backgroundSource recognized as marginal(s).
##
## ---- done(43.08 secs)!
## - - - - Checking expo_dir marginals ... Fish, Iter, Group, Gender, fish_proportionSource, Fishing, Age, Exposure_agent, concSource, Exposcen, expo_dirSource recognized as marginal(s).
## - - - - Processing expo_dir marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying expo_dir, found NAs in indices: Fishing, Age, Exposcen. They
## were automatically filled using fillna, which may result in a multiplied
## population. Please check your ovariable before using oapply.
## done!
## - - - - Evaluating expo_indir ...
## - - - - - Processing expo_dir marginal collapses ... done!
## - - - - - Evaluating t0.5 ...
##
## done(0 secs)!
## - - - - - Checking t0.5 marginals ... Exposure_agent, Iter, t0.5Source recognized as marginal(s).
## - - - - - Evaluating f_ing ...
##
## done(0 secs)!
## - - - - - Checking f_ing marginals ... Exposure_agent, Iter, f_ingSource recognized as marginal(s).
## - - - - - Evaluating f_mtoc ...
##
## done(0 secs)!
## - - - - - Checking f_mtoc marginals ... Exposure_agent, Iter, f_mtocSource recognized as marginal(s).
## - - - - - Evaluating BF ...
##
## done(0 secs)!
## - - - - - Checking BF marginals ... Exposure_agent, BFSource recognized as marginal(s).
##
## ---- done(6.93 secs)!
## - - - - Checking expo_indir marginals ... Iter, Gender, Fishing, Age, Exposure_agent, Exposcen, expo_dirSource, f_ingSource, t0.5Source, f_mtocSource, BFSource, Exposure, expo_indirSource recognized as marginal(s).
## - - - - Processing expo_indir marginal collapses ... done!
##
## --- done(1.44 mins)!
## - - - Checking exposure marginals ... Iter, Gender, Fishing, Age, Exposure_agent, Exposcen, Exposure, expo_dirSource, exposureSource recognized as marginal(s).
## - - - Processing exposure marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying exposure, found NAs in indices: Gender. They were automatically
## filled using fillna, which may result in a multiplied population. Please check
## your ovariable before using oapply.
## done!
## - - - Evaluating BW ...
##
## done(0 secs)!
## - - - Checking BW marginals ... BWSource recognized as marginal(s).
##
## -- done(1.52 mins)!
## - - Checking dose marginals ... Iter, Gender, Fishing, Age, Exposure_agent, Exposcen, Exposure, Scaling, exposureSource, BWSource, doseSource recognized as marginal(s).
## - - Processing dose marginal collapses ... done!
## - - Evaluating ERF ...
## - - - Evaluating ERF_env ...
##
## done(0.03 secs)!
## - - - Checking ERF_env marginals ... Exposure_agent, Response, Subgroup, Exposure, ER_function, Scaling, Exposure_unit, Observation, Iter, ERF_envSource recognized as marginal(s).
## - - - Evaluating ERF_omega3 ...
##
## done(0.02 secs)!
## - - - Checking ERF_omega3 marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, Iter, ERF_omega3Source recognized as marginal(s).
## - - - Evaluating ERF_mehg ...
##
## done(0 secs)!
## - - - Checking ERF_mehg marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, Iter, ERF_mehgSource recognized as marginal(s).
## - - - Evaluating ERF_diox ...
##
## done(0.01 secs)!
## - - - Checking ERF_diox marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, Iter, ERF_dioxSource recognized as marginal(s).
## - - - Evaluating ERF_vit ...
##
## done(0 secs)!
## - - - Checking ERF_vit marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_vitSource recognized as marginal(s).
## - - - Evaluating ERF_micr ...
##
## done(0 secs)!
## - - - Checking ERF_micr marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_micrSource recognized as marginal(s).
## - - - Evaluating ERF_pfas ...
##
## done(0 secs)!
## - - - Checking ERF_pfas marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, Iter, ERF_pfasSource recognized as marginal(s).
## - - - Evaluating ERFchoice ...
##
## done(0 secs)!
## - - - Checking ERFchoice marginals ... Exposure_agent, Response, Scaling, Exposure, ER_function, ERFchoiceSource recognized as marginal(s).
##
## -- done(2.32 secs)!
## - - Checking ERF marginals ... Exposure_agent, Response, Exposure, ER_function, Scaling, Observation, Iter, ERFSource recognized as marginal(s).
## - - Processing ERF marginal collapses ... done!
## - - Evaluating RR ...
## - - - Processing dose marginal collapses ... done!
## - - - Processing ERF marginal collapses ... done!
##
## -- done(1.06 mins)!
## - - Checking RR marginals ... Exposure_agent, Response, ER_function, Scaling, Iter, ERFSource, Gender, Fishing, Age, Exposcen, Exposure, doseSource, RRSource recognized as marginal(s).
## - - Evaluating frexposed ...
##
## done(0 secs)!
## - - Checking frexposed marginals ... frexposedSource recognized as marginal(s).
## - - Evaluating P_illness ...
##
## done(0 secs)!
## - - Checking P_illness marginals ... Response, Illness, Age, P_illnessSource recognized as marginal(s).
##
## - done(3.85 mins)!
## - Checking PAF marginals ... Exposure_agent, Response, ER_function, Scaling, Iter, ERFSource, Gender, Fishing, Age, Exposcen, Exposure, doseSource, frexposedSource, incidenceSource, Adjust, RRSource, PAFSource recognized as marginal(s).
## - Processing PAF marginal collapses ...
## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying PAF, found NAs in indices: Adjust. They were automatically
## filled using fillna, which may result in a multiplied population. Please check
## your ovariable before using oapply.
## done!
##
## done(4.24 mins)!
## Checking BoDattr marginals ... Adjust, Response, Age, Gender, Iter, Exposure_agent, Fishing, BoDattrSource recognized as marginal(s).
if(FALSE) {
oprint(summary(amount,"mean"))
oprint(summary(BoD,marginals=c("Age","Response"),"mean"))
oprint(summary(BoD,marginals=c("Gender","Response"),"mean"))
oprint(summary(BoDattr,marginals=c("Age","Response"),"mean"))
oprint(summary(BoDattr,marginals=c("Exposure_agent","Response"),"mean"))
oprint(summary(BoDattr,marginals=c("Gender","Response"),"mean"))
oprint(summary(case_burden,"mean"))
oprint(summary(conc,"mean"))
oprint(summary(dose,"mean"))
oprint(summary(ERF,"mean"))
oprint(summary(expo_dir,"mean"))
oprint(summary(expo_dir,marginals="Exposure_agent"))
oprint(summary(expo_indir,"mean"))
oprint(summary(exposure,"mean"))
#oprint(summary(fish_proportion,"mean"))
oprint(summary(incidence,"mean"))
oprint(summary(PAF,"mean"))
oprint(summary(population,"mean"))
oprint(summary(RR,"mean"))
}
###################
# Graphs
tmp <- trim(amount)
tmp <- tmp[tmp$Fishing!="Vanhankaupunginlahti",]
tmp$Group <- factor(tmp$Group, levels=c("Infants","Toddlers","Other children","Adolescents",
"Adults", "Elderly"))
p <- ggplot(tmp, aes(x=Group, weight=amountResult, fill=Fish))+geom_bar()+
coord_flip()+
labs(
title="Kalansyönti Suomessa ikäryhmittäin",
y="Syönti (g/d)"
)
if(params$porvoo) {
p <- p + facet_grid(Fishing ~ Gender)
print(p)
ggsave("Kalansyönti Suomessa ja Porvoossa ikäryhmittäin.svg")
} else {
p <- p + facet_grid(Gender~ .)
print(p)
ggsave("Kalansyönti Suomessa ikäryhmittäin.svg")
}
## Saving 7 x 5 in image
plot_ly(trim(EvalOutput(total_amount)), x=~Kala, y=~total_amountResult, color=~Kala, type="bar") %>%
layout(yaxis=list(title="Kalan kokonaiskulutus Suomessa (milj kg /a)"), barmode="stack")
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
ggplot(conc_mehg@output, aes(x=conc_mehgResult, colour=Area))+stat_ecdf()+
scale_x_log10()+facet_wrap(~Fish, scales="free_x")
ggplot(trim(conc_pfas), aes(x=Fish, y=conc_pfasResult))+geom_point()+ # Inputed data for missing species.
coord_flip()
ggplot(conc_pfas@output, aes(x=conc_pfasResult, color=Compound, linetype=Area))+stat_ecdf()+
scale_x_log10()+
stat_ecdf(data=conc_eukalat@output, aes(x=conc_eukalatResult))+
scale_linetype_manual(values=c("dotted","solid","twodash"))+
labs(
title="PFAS concentration in fish in Finland",
x="PFAS concentration (ng/g fresh weight)",
y="Cumulative probability"
)
## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Removed 16 rows containing non-finite values (stat_ecdf).
# The code may produce some negative values, which are removed from the graph
ggsave("PFAS-pitoisuus kalassa Suomessa.svg")
## Saving 7 x 5 in image
## Warning in self$trans$transform(x): NaNs produced
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Removed 16 rows containing non-finite values (stat_ecdf).
ggplot(conc@output, aes(x=concResult, colour=Fish))+stat_ecdf()+
facet_wrap(~Compound, scales="free_x")
limits <- data.frame(
Exposure_agent = c("TEQ","MeHg","PFAS","Vitamin D"),
Type = c("TDI","TDI","TDI","RDI"),
Result = c(2*70/7, 1.3*70/7,4.4*70/7,10)
)
tmp <- EvalOutput(expo_dir, verbose=TRUE) # Calculated again because we want Group which is collapsed
## Evaluating expo_dir ...
## - Processing amount inputs ...
## Warning in ComputeDependencies(variable@dependencies, fillna = fillna, indent =
## indent + : Input checking amount failed! Error: undefined columns selected
## - Processing amount marginal collapses ... done!
##
## done(26.64 secs)!
## Checking expo_dir marginals ... Fish, Iter, Group, Gender, fish_proportionSource, Fishing, Age, Exposure_agent, concSource, Exposcen, expo_dirSource recognized as marginal(s).
tmp <- oapply(tmp,NULL,sum,c("Fish","fish_proportionSource","amountSource","concSource"))
## Warning in oapply(tmp, NULL, sum, c("Fish", "fish_proportionSource",
## "amountSource", : While oapplying expo_dir, found NAs in indices: Group,
## Fishing, Age, Exposcen. They were automatically filled using fillna, which may
## result in a multiplied population. Please check your ovariable before using
## oapply.
tmp <- tmp[tmp$Exposcen=="BAU" & !tmp$Exposure_agent %in% c("ALA", "EPA"),]
tmp$Group <- factor(tmp$Group, levels=c("Toddlers","Other children","Adolescents","Adults","Elderly"))
if(params$porvoo) {
tmp <- tmp[tmp$Exposure_agent=="PFAS",]
limits <- limits[limits$Exposure_agent=="PFAS",]
titl <- "PFAS-yhdisteiden saanti kalasta eri ryhmissä"
} else titl <- "Yhdisteiden saanti kalasta"
p <- ggplot(tmp@output, aes(x=expo_dirResult+0.5, colour=Group))+stat_ecdf()+
scale_x_log10()+
geom_vline(data=limits, aes(xintercept=Result, linetype=Type))+
geom_point(data = oapply(tmp,c("Group","Exposure_agent","Fishing"),mean)@output,
aes(x=expo_dirResult, y=0.1), shape=1, size=3, stroke=2)+
labs(title=titl,
x="Suora altistuminen päivässä (kala: g, rasvahapot: mg, D-vit: ug, PFAS: ng, TEQ: pg)",
y="kumulatiivinen todennäköisyys")
if(params$porvoo) {
p <- p + facet_grid(Fishing ~ .)
print(p)
ggsave("PFAS-yhdisteiden saanti kalasta Suomessa ja Porvoossa.svg")
} else {
p <- p + facet_wrap(~Exposure_agent, scales="free_x")
print(p)
ggsave("Yhdisteiden saanti kalasta Suomessa.svg")
}
## Saving 7 x 5 in image
ggplot(trim(oapply(exposure,NULL,mean,"Gender")), aes(x=Age, weight=exposureResult))+geom_bar()+
facet_wrap(~Exposure_agent, scales="free_y")+
labs(
title="Exposure to compounds",
y="(omega: mg/d; vit D: ug/d, PFAS: ng/d)"
)
cat("Kalaperäisiä tautitaakkoja Suomessa\n")
## Kalaperäisiä tautitaakkoja Suomessa
if(openv$N>1) {
tmp <- summary(ERF)
tmp <- data.frame(
Altiste = tmp$Exposure_agent,
Vaikutus = tmp$Response,
Annosvastefunktio = tmp$ER_function,
Skaalaus = tmp$Scaling,
Havainto = tmp$Observation,
Keskiarvo = as.character(signif(tmp$mean,2)),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)#[rev(match(lev, tmp$Exposure_agent)),]
oprint(tmp)
tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Response")))
tmp <- data.frame(
Altiste = tmp$Exposure_agent,
Alue = tmp$Fishing,
Keskiarvo = as.character(signif(tmp$mean,2)),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)#[rev(match(lev, tmp$Exposure_agent)),]
oprint(tmp)
tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Exposure_agent")))
tmp <- data.frame(
Terveysvaikutus = tmp$Response,
Alue = tmp$Fishing,
Keskiarvo = signif(tmp$mean,2),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)
oprint(tmp)
tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Exposure_agent","Response")))
tmp <- data.frame(
Terveysvaikutus = "Yhteensä",
Alue = tmp$Fishing,
Keskiarvo = signif(tmp$mean,2),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)
oprint(tmp)
}
## Altiste Vaikutus Annosvastefunktio Skaalaus Havainto
## 1 DHA Loss in child's IQ points ERS None ERF
## 2 DHA Loss in child's IQ points ERS None Threshold
## 3 Fish All-cause mortality RR None ERF
## 4 Fish All-cause mortality RR None Threshold
## 5 Fish Depression RR None ERF
## 6 Fish Depression RR None Threshold
## 7 MeHg Loss in child's IQ points ERS None ERF
## 8 MeHg Loss in child's IQ points ERS None Threshold
## 9 Omega3 Breast cancer RR None ERF
## 10 Omega3 Breast cancer RR None Threshold
## 11 Omega3 CHD2 mortality Relative Hill None ERF
## 12 Omega3 CHD2 mortality Relative Hill None Threshold
## 13 PFAS Immunosuppression ERS BW ERF
## 14 PFAS Immunosuppression ERS BW Threshold
## 15 TEQ Cancer morbidity yearly CSF BW ERF
## 16 TEQ Cancer morbidity yearly CSF BW Threshold
## 17 TEQ Sperm concentration ERS None ERF
## 18 TEQ Sperm concentration ERS None Threshold
## 19 TEQ Yes or no dental defect ERS None ERF
## 20 TEQ Yes or no dental defect ERS None Threshold
## 21 Vitamin D Vitamin D recommendation Step None ERF
## 22 Vitamin D Vitamin D recommendation Step None Threshold
## Keskiarvo X95.luottamusväli Keskihajonta
## 1 -0.0013 -0.0018 - -0.00081 2.6e-04
## 2 0 0 - 0 0.0e+00
## 3 1 1 - 1 4.8e-04
## 4 0 0 - 0 0.0e+00
## 5 0.99 0.99 - 1 1.7e-03
## 6 0 0 - 0 0.0e+00
## 7 0.69 0.022 - 1.4 3.7e-01
## 8 6.8 0.32 - 13 3.8e+00
## 9 1 1 - 1 2.7e-04
## 10 0 0 - 0 0.0e+00
## 11 -0.17 -0.25 - -0.098 4.1e-02
## 12 47 47 - 47 0.0e+00
## 13 0.024 0.0012 - 0.045 1.3e-02
## 14 0 0 - 0 0.0e+00
## 15 9.8e-06 4.5e-07 - 1.9e-05 5.8e-06
## 16 0 0 - 0 0.0e+00
## 17 6.1e-05 -1.9e-05 - 0.00014 3.9e-05
## 18 0 0 - 0 0.0e+00
## 19 0.0014 0.00032 - 0.0024 5.8e-04
## 20 0 0 - 0 0.0e+00
## 21 100 100 - 100 0.0e+00
## 22 10 10 - 10 0.0e+00
## Altiste Alue Keskiarvo X95.luottamusväli Keskihajonta
## 1 DHA BAU -1400 -8800 - 0 2400
## 2 Fish BAU -56000 -250000 - 0 75000
## 3 MeHg BAU 720 0 - 7900 3900
## 4 Omega3 BAU -14000 -48000 - -1.4 15000
## 5 PFAS BAU 13 0 - 79 22
## 6 TEQ BAU 1600 0 - 9100 2600
## 7 Vitamin D BAU -2200 -20000 - 0 5000
## Terveysvaikutus Alue Keskiarvo X95.luottamusväli Keskihajonta
## 1 All-cause mortality BAU -51000 -240000 - 0 70000
## 2 Breast cancer BAU -3500 -15000 - 0.014 4300
## 3 Cancer morbidity yearly BAU 1200 0 - 8300 2500
## 4 CHD2 mortality BAU -10000 -39000 - -1.4 12000
## 5 Depression BAU -5400 -23000 - -0.01 6300
## 6 Immunosuppression BAU 13 0 - 79 22
## 7 Loss in child's IQ points BAU -700 -7300 - 4800 4200
## 8 Sperm concentration BAU 150 0 - 1400 410
## 9 Vitamin D recommendation BAU -2200 -20000 - 0 5000
## 10 Yes or no dental defect BAU 180 0 - 1300 440
## Terveysvaikutus Alue Keskiarvo X95.luottamusväli Keskihajonta
## 1 Yhteensä BAU -72000 -3e+05 - 270 87000
tmp <- trim(BoDattr[BoDattr$Fishing=="BAU",])
ggplot(tmp, aes(x=Exposure_agent, weight=BoDattrResult, fill=Response))+geom_bar()+
labs(
title="Tautitaakka kalasta tekijöittäin",
x="Kalassa oleva tekijä",
y="Tautitaakka (DALY/a"
)
ggsave("Tautitaakka kalasta tekijöittäin.svg")
## Saving 7 x 5 in image
ggplot(tmp, aes(x=Age, weight=BoDattrResult, fill=Response))+geom_bar(position="stack")+
scale_x_discrete(breaks = levels(ages)[seq(1,length(ages),3)])+
labs(
title="Tautitaakka kalassa olevista tekijöistä",
x="Ikäryhmä",
y="Tautitaakka (DALY/a)"
)
ggsave("Tautitaakka kalassa olevista tekijöistä.svg")
## Saving 7 x 5 in image
tmp <- trim(BoDattr / population * 1000)
p <- ggplot(tmp, aes(x=Age, weight=Result, fill=Response))+geom_bar(position="stack")+
scale_x_discrete(breaks = levels(ages)[seq(1,length(ages),3)])+
labs(
title="Tautitaakka kalassa olevista tekijöistä per henkilö",
x="Ikäryhmä",
y="Tautitaakka (mDALY/hlö/a)"
)
if(params$porvoo) {
p <- p + facet_wrap(~Fishing)
oprint(p)
ggsave("Tautitaakka kalassa olevista tekijöistä per henkilö.svg")
}
################ Insight network
#gr <- scrape(type="assessment")
#objects.latest("Op_en3861", "makeGraph") # [[Insight network]]
#gr <- makeGraph(gr)
#export_graph(gr, "ruori.svg")
#render_graph(gr) # Does not work: Error in generate_dot(graph) : object 'attribute' not found
##################### Diagnostics
#objects.latest("Op_en6007", code_name="diagnostics")
#print(showLoctable())
#print(showind())
# Run all above
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.